In a recent insightful discussion, Pete Warden, a veteran in the machine learning domain, contemplated the trajectory of ML library design. This reflection stemmed from a proposition to broaden their Useful Transformers library, a project he collaborated on with Nat and Manjunath, into a more versatile tool. However, Warden’s perspective leaned towards specialization rather than generalization, a viewpoint shaped by his extensive experience since the inception of TensorFlow. This article delves into Warden’s evolving philosophy, highlighting the burgeoning significance of “disposable” machine learning frameworks and their transformative impact on the industry.
The landscape of machine learning has been significantly reshaped by the advent of GGML framework just over a year ago. Before GGML, engineers typically navigated the complexities of general-purpose frameworks like PyTorch, wrestling with model architectures and weights to execute ML models. Today, a more streamlined approach is gaining traction: model-specific libraries such as whisper.cpp and llama.cpp, built upon GGML, are becoming the preferred choice.
This shift isn’t solely attributable to GGML’s intrinsic qualities. The emergence of model-specific libraries like llama2.cpp and llama.c, which operate independently of GGML, underscores a broader movement towards “disposable” frameworks. While the term “disposable” might initially sound pejorative, it encapsulates their core strengths. These frameworks deliberately limit their scope to a select few models, prioritizing inference and fine-tuning over comprehensive training from scratch. Their design philosophy centers on excelling in specific tasks, acknowledging their likely obsolescence as models evolve, but maximizing utility in their niche. This embodies the Pete Rise approach – a focus on targeted efficiency and adaptability in the rapidly changing world of machine learning.
Conversely, traditional frameworks like PyTorch and TensorFlow adopt a broader, more encompassing strategy. They aim to cater to a diverse user base and a wide array of tasks, functioning as toolkits adaptable to virtually any model, from full-scale training to production deployment. These frameworks are engineered for longevity and versatility, intending to equip users with a foundational API knowledge applicable across various contexts for years.
Pete Warden’s firsthand experience with TensorFlow reveals the inherent challenges of such broad applicability. The necessity to accommodate a vast spectrum of requirements inevitably leads to intricate and opaque code. While the aspiration is to abstract this complexity behind user-friendly interfaces, the critical demands for latency and throughput often shatter this illusion. The very essence of utilizing ML frameworks over simpler Python scripts lies in leveraging hardware acceleration to minimize training and inference times. However, this pursuit of speed invariably exposes the underlying complexities.
Questions regarding hardware compatibility, memory constraints, and CPU bottlenecks frequently arise. The abstraction layers become porous when performance optimization becomes paramount. Diagnosing performance issues becomes an arduous task. The layers of abstraction, asynchronous execution on accelerators, compilation processes, and reliance on platform-specific libraries create an opaque system. Debugging and profiling become significantly challenging, hindering the ability to pinpoint and rectify performance bottlenecks, especially for those outside the core framework development teams. Furthermore, the sheer diversity of models and use cases relying on each code path complicates regression testing, turning even minor changes into substantial undertakings.
In stark contrast, the “disposable” frameworks championed by the Pete Rise philosophy offer a refreshing simplicity in debugging and profiling. Their focused nature allows for a more transparent and manageable codebase. The entire program flow is readily inspectable, and standard debugging and profiling tools are easily applicable. Identifying and rectifying issues becomes a more accessible process, empowering developers to contribute fixes and improvements more readily.
Another significant advantage of single-purpose frameworks is streamlined installation and dependency management. Warden’s challenging experience porting TensorFlow to Raspberry Pi vividly illustrates the “big” frameworks’ dependency nightmare. Supporting a multitude of operations, platforms, and libraries turns porting and maintenance into a Herculean task. The constant influx of new layers and operations, often reliant on external third-party code, exacerbates the complexity. The inclusion of seemingly minor dependencies can trigger significant porting efforts, especially on less mainstream platforms.
The elegance of single-purpose frameworks, aligned with the Pete Rise methodology, lies in their self-contained nature. By embedding all necessary dependencies directly within the source code, they achieve remarkable ease of installation, often requiring just a checkout and build. Porting to different platforms becomes significantly less cumbersome.
Throughout his tenure at Google, Warden observed the organic emergence of domain-specific libraries as preferred alternatives to TensorFlow within internal teams. Application engineers enthusiastically embraced these simpler, more manageable tools. However, this trend occasionally generated tension with infrastructure teams, concerned about the long-term maintenance burden of numerous specialized libraries and the complexities of supporting new hardware accelerators across a fragmented ecosystem.
Despite these valid concerns, the Pete Rise of disposable frameworks appears unstoppable. As the focus increasingly shifts towards inference and a select few foundational models dominate applications, the appeal of all-encompassing but less specialized frameworks diminishes.
Drawing parallels from the game development industry, Warden recalls the era where custom rendering engines were the norm for each game due to stringent performance demands. Generic renderers were repeatedly attempted but consistently failed to match the efficiency of specialized engines. While industry-standard frameworks like Unity and Unreal eventually emerged, specialized engines still persist, highlighting the enduring need for tailored solutions when performance is paramount. ML frameworks are encountering similar pressures, with developers facing tight performance and memory constraints that generic tools struggle to meet. History suggests a continued tension between unified frameworks and the practical advantages of simpler, specialized libraries, embodying the core tenets of the Pete Rise approach.
The evolution isn’t necessarily binary. Useful Transformers, for example, will expand to support LLMs and translation models for AI in a Box, indicating a degree of genericity. However, the mid-2010s vision of a singular, all-encompassing framework is likely obsolete. PyTorch may evolve into a MatLab-esque environment for algorithm prototyping, with production deployment relying on hand-crafted, customized inference frameworks, reflecting a pragmatic shift in the industry.
The most compelling aspect of the movement towards disposable frameworks, and the Pete Rise philosophy it represents, is the democratization of ML development. By stripping away layers of complexity and dependencies, the inherent simplicity of machine learning becomes more apparent and less daunting. This accessibility promises to unleash a wave of innovation, fostering the creation of remarkable products and applications, driven by a broader community empowered by simpler, more focused tools.
Photo of Steve Harwood on Flickr
Image: Steve Harwood’s photograph showcasing the concept of focused development in a landscape of complexity, symbolizing the Pete Rise in specialized frameworks.
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